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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Photoinduced Dynamics with Constrained Vibrational Motion: FrozeNM Algorithm

Ab initio molecular dynamics (AIMD) simulation, analyzed in terms of vibrational normal modes, is a widely used technique that facilitates understanding of complex structural motions and coupling between electronic and nuclear degrees of freedom. Usually, only a subset of vibrations is directly involved in the process of interest. The impact of these vibrations can be evaluated by performing AIMD simulations by selectively freezing certain motions. In this paper, we present frozen normal mode (FrozeNM), a new algorithm to apply normal-mode constraints in AIMD simulations, as implemented in the nonadiabatic excited state molecular dynamics code. We further illustrate its capacity by analyzing the impact of normal-mode constraints on the photoinduced energy transfer between polyphenylene ethynylene dendrimer building blocks. Our results show that the electronic relaxation can be significantly slowed down by freezing a well-selected small subset of active normal modes characterized by their contributions in the direction of energy transfer. The application of these constraints reduces the nonadiabatic coupling between electronic excited states during the entire dynamical simulations. Furthermore, we validate reduced dimensionality models by freezing all the vibrations, except a few active modes. Altogether, we consider FrozeNM as a useful tool that can be broadly used to underpin the role of vibrational motion in a studied process and to formulate reduced models that describe essential physical phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tomographic Sparse View Selection Using the View Covariance Loss

Standard computed tomography (CT) reconstruction algorithms such as filtered back projection (FBP) and Feldkamp-Davis-Kress (FDK) require many views for producing high-quality reconstructions, which can slow image acquisition and increase cost in non-destructive evaluation (NDE) applications. Over the past 20 years, a variety of methods have been developed for computing high-quality CT reconstructions from sparse views. However, the problem of how to select the best views for CT reconstruction remains open. In this paper, we present a novel view covariance loss (VCL) function that measures the joint information of a set of views by approximating the normalized mean squared error (NMSE) of the reconstruction. We present fast algorithms for computing the VCL along with an algorithm for selecting a subset of views that approximately minimizes its value. Our experiments on simulated and measured data indicate that for a fixed number of views our proposed view covariance loss selection (VCLS) algorithm results in reconstructions with lower NRMSE, fewer artifacts, and greater accuracy than current alternative approaches.

Lin, Jingsong [Purdue University]↗
DOE OSTI2021

Using a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-z's), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-z. For this work, we perform initial redshift fits on Subaru Strategic Program galaxies with deep grizy photometry using Trees for Photo-Z (TPZ) before applying a custom neural network classifier (NNC) tuned to select galaxies with (z phot – z spec )/(1 + z spec ) < 0.10. We consider four cases of training and test sets ranging from an idealized case to using data augmentation to increase the representation of dim galaxies in the training set. Selections made using the NNC yield significant further improvements in outlier fraction and photo-z scatter (σ z ) over those made with typical photo-z uncertainties. As an example, when selecting the best third of the galaxy sample, the NNC achieves a 35% improvement in outlier rate and a 23% improvement in σ z compared to using uncertainties from TPZ. For cosmology and galaxy evolution studies, this method can be tuned to retain a particular sample size or to achieve a desired photo-z accuracy; our results show that it is possible to retain more than a third of an LSST-like galaxy sample while reducing σ z by a factor of 2 compared to the full sample, with one-fifth as many photo-z outliers. For surveys like LSST that are not limited by shot noise, this method enables a larger number of tomographic redshift bins and hence a significant increase in the total signal to noise of galaxy angular power spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Multivariate autoregressive (MVAR) model estimation enables assessment of causal interactions in brain networks. However, accurately estimating MVAR models for high-dimensional electrophysiological recordings is challenging due to the extensive data requirements. Hence, the applicability of MVAR models for study of brain behavior over hundreds of recording sites has been very limited. Prior work has focused on different strategies for selecting a subset of important MVAR coefficients in the model to reduce the data requirements of conventional least-squares estimation algorithms. Here we propose incorporating prior information, such as resting state functional connectivity derived from functional magnetic resonance imaging, into MVAR model estimation using a weighted group least absolute shrinkage and selection operator (LASSO) regularization strategy. The proposed approach is shown to reduce data requirements by a factor of two relative to the recently proposed group LASSO method of Endemann et al (Neuroimage 254:119057, 2022) while resulting in models that are both more parsimonious and more accurate. The effectiveness of the method is demonstrated using simulation studies of physiologically realistic MVAR models derived from intracranial electroencephalography (iEEG) data. The robustness of the approach to deviations between the conditions under which the prior information and iEEG data is obtained is illustrated using models from data collected in different sleep stages. This approach allows accurate effective connectivity analyses over short time scales, facilitating investigations of causal interactions in the brain underlying perception and cognition during rapid transitions in behavioral state.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗
DOE OSTI2023

Genomic dissection of anthracnose resistance response in sorghum [Sorghum bicolor (L.) Moench]

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important grain crop behind maize, wheat, rice, and barley. Today, it is of interest as a source of fermentable sugars for the production of renewable fuels and chemicals, and as a source of biomass for co-firing. The productivity and profitability of sorghum are limited by several biotic constraints, most notably anthracnose caused by the fungal pathogen Colletotrichum sublineolum. The most cost-effective and environmentally benign strategy to control anthracnose is through the incorporation of resistance genes. Over the last three years, our research efforts have been directed to identify new sources of resistance in temperate adapted and tropical germplasm, and to delimited genomic regions associated with the observe anthracnose resistant response. Three biparental mapping populations derived from the resistant lines SC112-14, QL3 and IS18760 were evaluated for anthracnose resistance response in Texas, Georgia, Florida and Puerto Rico. In parallel, three high density recombination maps were constructed and used to identify resistant loci. Anthracnose resistant response in line SC112-14 is controlled by a major locus on chromosome 5. Segregation analysis of 1,500 progenies delimited the resistance locus on chromosome 5 to a 23-kb region harboring three candidate genes, including Sobic.005G17230 identified by GWAS of the sorghum association panel (SAP). The latter gene belongs to a family of genes encoding F-box proteins indicating that this resistance response involved in signaling cascades and transcriptional reprograming, rather than recognition of pathotype-associated molecular patterns. In contrast, anthracnose resistant response in lines QL3 and IS18760 is controlled by multiple small-effect genes. Greenhouse evaluation of a representative subset of the three mapping populations against nine pathotypes found that lines susceptible in the field could be resistant to a single pathotype in the greenhouse. Thus, the activation of a resistance response system by a single pathotype could not provide a broader resistance response against multiple pathotypes. The screening of 1,801 sweet sorghum accessions from the National Plant Germplasm System identified 654 accessions with Brix value larger than 10, which in turn was used to select a subset of 233 accessions for evaluation of anthracnose resistant response. Even though most of the accessions were not completely infected by anthracnose, 28 accessions were completely resistant against pathotypes from Texas, Georgia, Florida and Puerto Rico. Genotyping-by-sequencing analysis of this subset identified 157,843 single nucleotide polymorphisms. Population structure analysis of the subset based on a subset of 2,345 unlinked SNPs found that the genetic diversity could be divided into four populations. The genetic relatedness among accessions within populations suggests most of the resistant germplasm may contain few different resistance sources. These resistance sources present in sweet sorghum germplasm could expedite the development of new resistant sweet sorghum cultivars and hybrids by avoiding time-consuming introgression breeding approaches with non-sweet sorghums serving as donor of the resistance alleles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica

Abstract Genome-wide functional genetic screens have been successful in discovering genotype-phenotype relationships and in engineering new phenotypes. While broadly applied in mammalian cell lines and in E. coli , use in non-conventional microorganisms has been limited, in part, due to the inability to accurately design high activity CRISPR guides in such species. Here, we develop an experimental-computational approach to sgRNA design that is specific to an organism of choice, in this case the oleaginous yeast Yarrowia lipolytica . A negative selection screen in the absence of non-homologous end-joining, the dominant DNA repair mechanism, was used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a. This genome-wide data served as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuide’s ability to accurately predict high activity sgRNAs. DeepGuide provides an organism specific predictor of CRISPR guide activity that with retraining could be applied to other fungal species, prokaryotes, and other non-conventional organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Ecologically-Informed Precision Conservation: A framework for increasing biodiversity in intensively managed agricultural landscapes with minimal sacrifice in crop production

Conservation actions are urgently needed to tackle biodiversity loss in intensively managed agricultural landscapes. Production lands are usually heterogeneous and contain low-yield areas that can be set aside for biodiversity conservation without serious yield losses. Here, we introduce Ecologically-Informed Precision Conservation, a framework that integrates yield mapping and ecological theory to select the best areas to create new set-asides while ensuring high crop yields at the farm/landscape level. Long-term yield maps can be generated using globally available satellite data and basic information on field/farm crop yield from farmers. Ecological principles are then used to select the subset of areas with the highest potential for biodiversity conservation by prioritising those that increase connectivity, maximise habitat heterogeneity and decrease landscape grain size. Here, the created non-crop habitats can be permanent and thus ensure biodiversity support over time. In addition, agricultural management efficiency can be enhanced by improving field shapes. The framework provides the basis for a practical, user-friendly tool that informs all interested stakeholders on how to rationalise existing agricultural landscapes using already-existing farming systems and available technologies. High cost-effectiveness from an economic and conservation perspective, along with the creation of heterogeneous non-crop habitats, make our framework a promising solution to re-design agricultural landscapes.

Precision agriculture↗
DOE OSTI2022

Approximating a linear multiplicative objective in watershed management optimization

Implementing management practices in a cost-efficient manner is critical for regional efforts to reduce the amount of pollutants entering the Chesapeake Bay. We study the problem of selecting a subset of practices that minimizes pollutant load—subject to budgetary and environmental constraints—as simulated in a widely used regulatory watershed model. Mimicking the computation of pollutant load in the regulatory model, we formulate this problem as a continuous optimization model with a linear multiplicative objective function and linear constraints. To lay the groundwork for incorporating additional stakeholder requirements in the future, especially those that would require integer variables, we present and study a continuous linear optimization model that approximates the nonlinear model. The linear model, which requires an exponential number of variables, arises naturally as an alternative model for the same underlying physical process. We examine the theoretical behavior of these optimization models and investigate restrictions of the linear model to handle its large number of variables. Through extensive computational tests on real and randomly generated instances, we demonstrate that the linear model and its restrictions provide optimal solutions close to those of the nonlinear model in practice, despite poor approximation properties in the worst case. We conclude that the linear model—together with our approach to handling its large number of variables—provides a viable framework from which to extend the optimization model to better meet the needs of the Chesapeake Bay watershed management stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Qubit Assignment Using Time Reversal

As quantum computers with large numbers of qubits become increasingly available, experiments executed on a given device may not utilize all available qubits. In this case, the outcome of executing a quantum program will depend on the ability to efficiently select a subset of high-performing physical qubits. For any given quantum program and device there are many ways to assign physical qubits for execution of the program, and assignments will differ in performance due to the variability in quality across qubits and entangling operations on a single device. Evaluating the performance of each assignment using fidelity estimation introduces significant experimental overhead and will be infeasible for many applications, while relying on standard device benchmarks provides incomplete information about the performance of any specific program. Furthermore, the number of possible assignments grows combinatorially in the number of qubits on the device and in the program, motivating the use of heuristic optimization techniques. We demonstrate a practical solution to the problem of qubit assignment by using simulated annealing with a cost function based on the Loschmidt echo, a diagnostic that measures the reversibility of a quantum process. We provide theoretical justification for this choice of cost function by demonstrating that the optimal qubit assignment coincides with the optimal qubit assignment based on state fidelity in the weak error limit, and we provide experimental justification using diagnostics performed on Google’s superconducting qubit devices. We then establish the performance of simulated annealing for qubit assignment using classical simulations of noisy devices as well as optimization experiments performed on a quantum processor. Our results demonstrate that the use of Loschmidt echoes and simulated annealing provides a scalable and flexible approach to optimizing qubit assignment on near-term hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. Furthermore, this study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.

42 ENGINEERING↗
DOE OSTI2020

Harnessing the power of ab initio calculations, distributed computing and machine learning to efficiently locate extreme molecules for use in carbon-based solar cells (Final Technical Report)

The use of high-throughput virtual screening (HTVS) tools is a powerful tool to expedite the materials discovery of commercially relevant materials. In previous years, our group has developed a molecular discovery platform to generate libraries in order to obtain suitable candidates for different applications, starting from the Harvard Clean Energy Project. This platform is suitable to test in-silico on traditional supercomputing clusters and shared resources, for example, in the IBM World Community Grid. In this project, we used the molecular discovery platform to create and screen a library of candidates of organic photovoltaic (OPVs) molecules. Based on a set of candidates created with combinations of molecular moieties, we were able to filter, by conformation stability, the energy of electronic orbitals and approximated power conversion efficiencies (PCE). To improve the predictions of orbital energies calculated and the PCEs, we used Gaussian Process regression and two sets of molecules. These sets correspond to electronic structure calculations of a higher level of theory and experimental PCE values, respectively. Finally, we selected a subset of the best candidates (molecules with a PCE higher than 10%) to understand its absorbance properties with TD-DFT. This project has demonstrated the capabilities of our molecular discovery platform for HTVS. Finally, machine learning can help us to introduce more complex effects included in bulk conditions and computational intensive calculations on models.

14 SOLAR ENERGY↗
DOE OSTI2020

Initial Exploration of the Use of Mixed Precision in Iterative Solvers (B639388 Subcontract Quarter I Report)

The first quarter of the project was primarily spent identifying potential projects at the intersection of finite precision analysis, mixed precision computation, and Krylov subspace methods. We summarize our findings in the remainder of the document. Other activities include attending biweekly xSDK meetings as well as contributing material to the technical report and journal versions of the multiprecision landscape paper. The subsequent quarter will be spent selecting a subset of the described projects to focus on, performing initial numerical experiments to evaluate the potential for the use of mixed precision, and developing initial theoretical analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Recommendations to Improve the Nuclear Regulatory Commission Reactor Licensing and Approval Process

Due to the urgency around climate change and associated goals for clean energy transition, and Battelle Energy Alliance's (BEA) and Idaho National Laboratory's (INL) role as the nation's nuclear energy laboratory, numerous stakeholders have asked for BEA’s thoughts and recommendations to reduce the time and costs associated with licensing new reactors at the U.S. Nuclear Regulatory Commission (NRC). As an M&O contractor for an FFRDC, BEA is a long-term partner with the Government in seeking to achieve clean energy goals, yet has a level of independence needed to appropriately evaluate this topic. The views herein are informed by extensive BEA experience supporting nuclear energy endeavors including ongoing discussions with current and former regulators, applicants, and licensees. With this background in mind, the United States benefits from having an agency such as the NRC, which is viewed internationally as the leader in nuclear safety licensing and regulation. Nonetheless, while acknowledging the important nuclear safety role satisfied by the NRC, it is apparent that one of the most significant time and resource intensive activities for new reactor developers is the NRC licensing process. The time and cost to obtain NRC licenses add significant financial stress for new reactor projects and may result in abandonment of projects or failure to even begin new projects. The challenge is particularly acute for advanced reactors which may raise unique or new regulatory questions and may be smaller in size, resulting in a much higher proportional impact from regulatory and cost challenges. This situation presents a particularly troublesome risk for the nation given the urgency in which utilities are working to transition to clean, non-carbon-emitting energy sources like nuclear energy. Reforms to the NRC licensing process have the potential to greatly increase certainty and support the successful progress of new reactors. The NRC can retain its world-class nuclear safety reputation while becoming a world leader for regulatory efficiency and a critical enabler to the clean energy transition. This report describes potential NRC reforms, focusing on those with a statutory connection. Recognizing the potential tradeoffs with any proposed changes, the report attempts to highlight those considerations in the analysis of the reforms. The recommendations are presented as a set of options for consideration. Unless noted, they are independent options, offering stakeholders the option to select a subset for further consideration. Although difficult to calculate precise time improvements for some of the changes, the reforms have the potential for substantial improvements, perhaps even by a factor of two.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Miscanthus sacchariflorus (MSA)↗
DOE OSTI2025

Aided Active Learning (AAL) for Enhanced Critical Heat Flux Prediction

Accurate prediction of critical heat flux (CHF) is crucial for the safe and efficient operation of nuclear reactors. Traditional CHF modeling methods often require extensive experimental data, which are hard to obtain. This study introduces the Aided Active Learning (AAL) framework, which strategically minimizes data requirements without sacrificing model accuracy. Unlike conventional Active Learning (AL), AAL introduces an additional step of randomly selecting a subset from the sample pool before applying the query strategy. To evaluate the performance of AAL, two query strategies—uncertainty-based sampling and error-reduction sampling—were evaluated across the following models: random forest (RF), feedforward neural network (FNN), and variational feedforward neural network (vFNN). The proposed framework demonstrated that AAL effectively reduces the number of training samples needed to achieve comparable predictive accuracy. For the RF model, AL required only 710 samples to achieve an R2 score of 0.98, as compared to the 4,785 samples needed by random sampling. Similarly, the FNN model achieved the same R2 score with just 355 samples when using AL, a significant improvement over the 825 samples required by random sampling. In case of uncertainty-based sampling strategy, vFNN attained an R2 of 0.98 with 3,420 samples, reducing the sample requirement by 47% relative to the 6,440 samples needed for random sampling. Its performance suggests that larger training data are required to fully leverage its uncertainty quantification capabilities.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Impacts of Solvent Washing on the Electrochemical Remediation of Commercial End-of-Life Cathodes

Changes to surface structure and chemistry occurring throughout the functional lifetime of lithium-ion batteries (LIBs) may impact the effectiveness of end-of-life rejuvenation methods. Solvent washing prior to electrochemical relithiation is shown to both increase relithiation efficacy and beneficially alter the interfacial chemistry of heavily degraded industrial cathode material. Four common solvents (acetone, diethyl carbonate, isopropyl alcohol, propylene carbonate) are employed to investigate the role of varying physicochemical solvent properties on the mechanism of capacity recovery. Electrochemical (dQ/dV, EIS), structural (XRD), and chemical (SPME-GC-MS) analysis techniques are employed to comprehensively analyze solvent-cathode interactions. Highly nucleophilic solvents (acetone, DEC) are found to reduce cathode charge-transfer impedance and enable stable impedance growth throughout subsequent cycling. The use of nucleophilic solvents under mechanically aggressive washing conditions may also enable the reintroduction of bulk lattice oxygen, thereby restoring anionic redox capacity. Further, the four solvents are found to selectively remove a subset of surface species from the aged cathode material, including residual electrolyte, additives, and electrolyte-additive reaction products, which are qualitatively analyzed. Surface species removal by each solvent is correlated with the electrochemical performance of the correspondingly washed cathode, highlighting the importance of an optimized washing protocol to effective remediation in the context of direct LIB recycling. For the material under study, the use of a simple acetone washing protocol prior to electrochemical relithiation enables up to 174% capacity recovery relative to unwashed/relithiated black mass.

ADVANCED PROPULSION SYSTEMS↗